Affiliation:
1. University of North Carolina at Greensboro, Greensboro, NC
Abstract
The success of Wireless Sensor Networks (WSNs) depends largely on efficient information delivery from target areas toward data sinks. The problem of data forwarding is complicated by the severe energy constraints of sensors in WSNs. In this work, we propose and analyze a data forwarding scheme, termed Multihop/Direct Forwarding (MDF), for WSNs where sensor nodes forward data traffic toward a common data sink. In the MDF scheme, a node splits outgoing traffic into at most two branches: one is sent to a node that is
h
units away; the other is sent directly to the data sink. The value of
h
is chosen to minimize the overall energy consumption of the network. The direct transmission is employed to balance the energy consumption of nodes at different locations and to avoid the so-called hot spot problem in data forwarding. In order to calculate its traffic splitting ratio, a node only needs to know the distance toward the common data sink and that of the farthest node. Our analytical and simulation results show that the MDF scheme performs close to, in terms of energy efficiency and network lifetime, the optimum data forwarding rules, which are more complex and computation intensive.
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications
Cited by
33 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Dynamic Slimmable Network;2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2021-06
2. Hierarchical Layout-Aware Graph Convolutional Network for Unified Aesthetics Assessment;2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2021-06
3. Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation;2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2021-06
4. Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning;2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2021-06
5. Decoupled Dynamic Filter Networks;2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2021-06